{
  "field_id": "F012",
  "field": "adversarial robustness",
  "directions": [
    {
      "direction_id": "G0",
      "name": "Empirical robustness assessment and attack stress testing",
      "scope": "Examine whether empirical conclusions about model robustness are reliable, including evaluation protocols, comparative measurement, and judgments of attack effectiveness, as well as improving attack search to find adversarial examples missed by evaluations."
    },
    {
      "direction_id": "G1",
      "name": "Evasion under transfer, feedback, or reuse constraints",
      "scope": "Construct attacks that remain effective when each target input cannot be fully optimized or the target model cannot be accessed. Transfer across models or other settings, limited target feedback, and reuse across inputs are distinct possible approaches."
    },
    {
      "direction_id": "G2",
      "name": "Physically realizable perception attacks",
      "scope": "Attack perception systems through deployable objects, illumination, or interventions in the imaging process, while remaining effective under physical variation."
    },
    {
      "direction_id": "G3",
      "name": "Validity-constrained edits to predictive inputs",
      "scope": "Find edits that cause errors in predictive tasks while the inputs continue to satisfy constraints on linguistic meaning, fluency, semantics, or functionality."
    },
    {
      "direction_id": "G4",
      "name": "Adversarial reliability of graph predictions",
      "scope": "Study why predictions fail when graph relationships or propagated signals are maliciously changed, and characterize and improve reliability by selecting attack structures or designing graph defenses."
    },
    {
      "direction_id": "G5",
      "name": "Security of learning and adaptation signals",
      "scope": "Characterize or resist learning errors caused by attacker control of training data, labels, feedback, or adaptation batches."
    },
    {
      "direction_id": "G6",
      "name": "Attacks on generative-system behavior",
      "scope": "Use prompts or multimodal inputs to alter generated content, tool behavior, or safety controls, rather than primarily causing errors in predicted labels."
    },
    {
      "direction_id": "G7",
      "name": "Building and adapting robust predictors",
      "scope": "Build and maintain the adversarial resilience of predictive models through training objectives and cost control, feature organization, downstream adaptation of pretrained models, or architectural design and explanation."
    },
    {
      "direction_id": "G8",
      "name": "Defensive handling of adversarial inputs and predictions",
      "scope": "Protect system decisions when inputs may have been manipulated by detecting or repairing them, or by using complementary prediction mechanisms. This also includes training and guarantees designed specifically around localized adversarial patches."
    },
    {
      "direction_id": "G9",
      "name": "Provable robustness of trained predictions",
      "scope": "Establish or train for verifiable guarantees of prediction robustness through either deterministic bounds and verifiers or randomized-smoothing certificates formed by aggregating predictions under added noise."
    },
    {
      "direction_id": "G10",
      "name": "Foundations of adversarial vulnerability and learnability",
      "scope": "Explain attainable robustness, robust risk, learning and generalization properties, and structural causes of vulnerability, rather than primarily delivering a specific attack, a deployed defense, or a certificate for a single model."
    }
  ]
}